[3]:
import preprocessing.Preprocessing as pp
import classes.transportnetwork as tn
from visualisation.visualisation import *
from characterisation.degree import *
from characterisation.centrality import *
from characterisation.distance import *
from robustness_analysis.robustness import *
from ML.embedding import *
from clustering.cluster import *
from GNN.data import *
from GNN.model import *
from GNN.run import *
Create the network from a GTFS file#
[2]:
G = pp.create_network_from_GTFS('data/gtfs_3')
Network creation:
100%|██████████| 144634/144634 [00:29<00:00, 4831.40it/s]
Create the transport network object#
[3]:
TN = tn.TransportNetwork(G, pos_argument=["lon", "lat"], time_arguments=["departure_time", "arrival_time"])
[4]:
print(TN)
Graph type: <class 'networkx.classes.graph.Graph'>
- Number of nodes: 613
|--- lon
|--- lat
- Number of edges: 776
|--- route_id
|--- trip_id
|--- euclidian_distance
|--- distance
|--- arrival_time
|--- departure_time
Graph type: <class 'networkx.classes.digraph.DiGraph'>
- Number of nodes: 613
|--- lon
|--- lat
- Number of edges: 1333
|--- route_id
|--- trip_id
|--- euclidian_distance
|--- distance
|--- arrival_time
|--- departure_time
Graph type: <class 'networkx.classes.multigraph.MultiGraph'>
- Number of nodes: 613
|--- lon
|--- lat
- Number of edges: 81868
|--- route_id
|--- trip_id
|--- euclidian_distance
|--- distance
|--- arrival_time
|--- departure_time
Graph type: <class 'networkx.classes.multidigraph.MultiDiGraph'>
- Number of nodes: 613
|--- lon
|--- lat
- Number of edges: 140098
|--- route_id
|--- trip_id
|--- euclidian_distance
|--- distance
|--- arrival_time
|--- departure_time
Visualize the network#
[5]:
map_network(TN)
[6]:
map_dynamic_network(TN, step=100)
Characteristics of the network#
Degree analysis#
[7]:
degree_analysis = compute_node_degree_analysis(TN, data=False)
degree_analysis
[7]:
{'min_in_degree': 0,
'max_in_degree': 2170,
'min_out_degree': 0,
'max_out_degree': 2170,
'avg_out_degree': 228.54486133768353}
[8]:
degree_analysis = compute_node_degree_analysis(TN, data=True)
degree_analysis
[8]:
{'HTSB0': [255, 257],
'BREU0': [504, 504],
'JOUA1': [504, 504],
'PLGA0': [917, 917],
'CARA1': [518, 518],
'TOUR0': [518, 518],
'MAUB0': [1131, 1131],
'GRBY0': [1838, 1845],
'ECHM0': [2170, 2170],
'MBAY0': [2170, 2170],
'BASQ1': [839, 838],
'SOUS0': [807, 807],
'BALI1': [807, 807],
'CCI0': [504, 504],
'ARPO0': [709, 709],
'FORU1': [504, 504],
'BAB20': [504, 504],
'ARTO0': [830, 830],
'LBAR0': [830, 830],
'LEMB0': [913, 913],
'BRNM0': [1183, 1183],
'MANG0': [710, 710],
'LOUI0': [510, 510],
'MOUR0': [510, 510],
'CHSN0': [510, 510],
'AGUI1': [510, 510],
'EURM0': [510, 510],
'AILL0': [1341, 1341],
'SCHV0': [618, 618],
'PALA0': [704, 704],
'MBIA0': [288, 285],
'CONT0': [698, 698],
'SCHM0': [612, 612],
'BASQ0': [1187, 1190],
'GARR0': [252, 254],
'GEND0': [393, 393],
'FEMM0': [495, 495],
'HDVT0': [495, 495],
'CLAI0': [390, 390],
'ECLL0': [390, 390],
'PCAS0': [562, 562],
'OCEA0': [632, 632],
'COTE0': [399, 399],
'CAMA0': [399, 399],
'MTRS0': [613, 613],
'CITA0': [613, 613],
'PAUL0': [1146, 1146],
'STLE0': [942, 942],
'JDGR0': [490, 490],
'HOPI0': [536, 536],
'LYCE0': [539, 539],
'MRRC0': [341, 353],
'SAIN0': [199, 200],
'OSTE0': [399, 399],
'HARG0': [399, 399],
'HABA0': [399, 399],
'HOUR0': [399, 399],
'ETIE0': [399, 399],
'AVRI0': [399, 399],
'CAM0': [400, 403],
'HARA0': [473, 472],
'LART0': [399, 399],
'BURU0': [399, 399],
'DESC0': [399, 399],
'LAVG0': [399, 399],
'MUSC0': [399, 399],
'JAUR0': [603, 603],
'BOUR0': [603, 603],
'FERR0': [767, 767],
'ALSA0': [767, 767],
'LACH0': [411, 411],
'MARO0': [411, 411],
'VILL0': [413, 415],
'BEYR0': [572, 572],
'UNIO0': [409, 409],
'COTO0': [399, 399],
'LAVI0': [399, 399],
'GLAC0': [399, 399],
'AERO0': [504, 505],
'PARM0': [392, 392],
'VIAD0': [306, 306],
'ABBA0': [92, 92],
'IZAR0': [46, 44],
'NEGR0': [1019, 1022],
'MANG1': [199, 199],
'STLE1': [208, 208],
'BARB1': [72, 72],
'REDU1': [975, 975],
'ANDR0': [767, 767],
'ARSE0': [767, 767],
'GENI0': [564, 564],
'LAUG0': [580, 580],
'CASS0': [692, 692],
'LOEB0': [547, 547],
'CONS0': [547, 547],
'CITE0': [547, 547],
'DARR0': [547, 547],
'VALL0': [334, 334],
'POLO0': [334, 334],
'GAUD0': [334, 334],
'LAFO0': [334, 334],
'DSBS0': [326, 326],
'NUMA0': [326, 326],
'FUTA0': [331, 331],
'COUR0': [384, 384],
'CHEN0': [550, 550],
'BOIB0': [550, 550],
'LOUS0': [332, 332],
'GOCH0': [332, 332],
'PECH1': [332, 332],
'CINQ0': [545, 545],
'GRCH0': [326, 326],
'QATR0': [326, 326],
'LARO0': [326, 326],
'ESTA0': [326, 326],
'MADE0': [326, 326],
'CITS0': [437, 438],
'EDOU1': [897, 898],
'FOCH0': [439, 439],
'JARD0': [874, 874],
'FONT0': [331, 331],
'MART0': [330, 327],
'TAMA0': [326, 326],
'MICH0': [326, 326],
'SIMM0': [326, 326],
'PETR0': [326, 326],
'LARR0': [326, 326],
'SOLE0': [326, 326],
'LARU0': [326, 326],
'FJAM0': [326, 326],
'BOIS0': [502, 502],
'ATAB0': [209, 209],
'ZAIR0': [209, 209],
'CHPT0': [209, 209],
'MOUS1': [209, 209],
'IRAT0': [133, 128],
'LRLD0': [395, 395],
'COUR1': [164, 164],
'IKEA0': [285, 289],
'ATUR0': [347, 348],
'ZADI0': [288, 288],
'MURG0': [146, 146],
'CAST1': [296, 296],
'DHAR1': [295, 295],
'FHAR1': [295, 295],
'AROT1': [295, 295],
'OURO1': [295, 295],
'DASB1': [292, 292],
'PERL1': [292, 292],
'QUIE1': [292, 292],
'BELH0': [343, 344],
'CHEA0': [344, 344],
'MADU0': [344, 344],
'CAMP0': [344, 344],
'POYD0': [344, 344],
'SABA1': [303, 303],
'SABA0': [303, 303],
'CANA0': [132, 132],
'HAUS0': [132, 132],
'BAHI1': [132, 132],
'LBAB0': [132, 132],
'EBAB0': [132, 132],
'KBAB0': [132, 132],
'MURG1': [146, 146],
'SMOR0': [391, 391],
'ESPA0': [391, 391],
'ROND0': [317, 317],
'PICH0': [215, 215],
'CHIC0': [215, 215],
'CAND0': [215, 215],
'ARAG0': [215, 215],
'MONT0': [210, 210],
'LATA0': [280, 280],
'PIQU0': [280, 280],
'PITA0': [280, 280],
'BART0': [283, 283],
'BARJ0': [284, 284],
'BOUG0': [307, 307],
'MUTA0': [191, 191],
'BRAM0': [191, 191],
'DUPR0': [191, 191],
'BERD0': [362, 362],
'DELV0': [214, 214],
'LAHA0': [214, 214],
'AMA0': [106, 106],
'BRID0': [214, 214],
'CUZA0': [213, 213],
'ARIT0': [213, 213],
'PLAI0': [213, 213],
'ARTE0': [382, 382],
'MUSE0': [382, 382],
'GALO0': [382, 382],
'MNVA0': [601, 601],
'MAIS0': [105, 105],
'BIBL0': [105, 105],
'PLAM0': [328, 328],
'ENDA0': [325, 326],
'BERN1': [323, 323],
'AMAD0': [108, 108],
'POLI0': [204, 204],
'MROL0': [204, 204],
'GUIL0': [201, 201],
'PNEU0': [201, 201],
'HVBC': [201, 201],
'ANDB0': [140, 140],
'PLAG0': [361, 362],
'MRNL0': [544, 544],
'FONL1': [373, 373],
'SABL0': [373, 373],
'PINS0': [373, 373],
'DOUA1': [204, 204],
'ABB0': [210, 209],
'PORT0': [209, 209],
'PIGN0': [209, 209],
'COMP0': [209, 209],
'BELL0': [209, 209],
'STMI0': [204, 204],
'CIGA0': [477, 475],
'RANS0': [204, 204],
'GOUG1': [204, 204],
'ACAC0': [425, 425],
'LAMO0': [209, 209],
'MSSO0': [209, 209],
'TIVO0': [209, 209],
'HARD0': [205, 205],
'AREN0': [205, 205],
'CAPU0': [205, 205],
'TRIB0': [205, 205],
'GUST0': [204, 204],
'TILO0': [204, 204],
'GOME0': [204, 204],
'CHAL0': [204, 204],
'GRAN0': [204, 204],
'CCFR0': [204, 204],
'FORT0': [208, 208],
'TROU0': [208, 208],
'BAYA0': [208, 208],
'HAME0': [208, 208],
'ARRO0': [208, 208],
'LAPI0': [208, 208],
'SEQU0': [207, 205],
'EHPA0': [208, 208],
'STET0': [208, 208],
'HNAV0': [208, 208],
'DEYR0': [208, 208],
'HTSB1': [158, 166],
'VAGU0': [175, 175],
'STEM0': [176, 177],
'LECL1': [175, 175],
'PAQU0': [169, 169],
'MEGN1': [169, 169],
'PRIM1': [169, 169],
'DBAR0': [169, 169],
'ADUF0': [169, 169],
'MELV0': [221, 221],
'MAIG0': [221, 221],
'SUTA0': [245, 245],
'BRDE0': [245, 245],
'CDSU0': [91, 92],
'HOND0': [90, 90],
'CAMN0': [90, 90],
'JUVI0': [90, 90],
'TRCR0': [84, 82],
'CMPT0': [122, 122],
'DONR0': [283, 283],
'PETI0': [310, 310],
'FLOQ0': [218, 218],
'KLEB0': [218, 218],
'SABO0': [218, 218],
'TAMM0': [218, 218],
'LABH0': [218, 218],
'RANQ1': [218, 218],
'PARM1': [218, 218],
'7PUI0': [218, 218],
'HOUN0': [218, 218],
'HBRD0': [218, 218],
'ERMI0': [218, 218],
'TRLA0': [218, 218],
'CNTA0': [222, 221],
'FRGE0': [225, 225],
'MSLT0': [225, 225],
'CHOI0': [223, 223],
'ELHO0': [223, 223],
'LRBT0': [218, 218],
'BAB30': [220, 219],
'PSOL0': [221, 221],
'BELM0': [221, 221],
'BUTT0': [221, 221],
'RENA0': [221, 221],
'CAMI0': [221, 221],
'FINE0': [392, 392],
'LESP0': [392, 392],
'MONB0': [392, 392],
'CORD0': [224, 223],
'RENE0': [226, 226],
'BLAN0': [226, 226],
'PIBA0': [226, 226],
'BECA0': [226, 226],
'REDO0': [64, 64],
'LAZA1': [93, 93],
'NORO0': [264, 264],
'PTPL0': [264, 264],
'BRIS0': [264, 264],
'SEMA0': [261, 261],
'BARR0': [216, 216],
'LORE0': [172, 171],
'LABO0': [171, 171],
'POUT0': [171, 171],
'HARI0': [171, 171],
'FAL0': [173, 174],
'CHAU0': [171, 171],
'BARC0': [171, 171],
'HARC0': [171, 171],
'BTZC0': [171, 171],
'BOUL0': [294, 293],
'TMAR0': [245, 245],
'MILA0': [245, 245],
'RIVA0': [245, 245],
'BORD0': [315, 315],
'SLAS0': [315, 315],
'HELI0': [245, 245],
'CHAM0': [122, 122],
'JARD1': [122, 122],
'MADE1': [251, 251],
'TRIG0': [171, 171],
'GOLB0': [171, 171],
'COPE0': [171, 171],
'MERG0': [171, 171],
'FALA0': [171, 171],
'CHAP0': [171, 171],
'CORS0': [171, 171],
'MADR0': [171, 171],
'GOLF0': [171, 171],
'DUNE0': [171, 171],
'CAVA0': [171, 171],
'CHIB0': [171, 171],
'LAZA0': [176, 176],
'FAIS0': [176, 176],
'OROK0': [176, 176],
'TUC0': [176, 176],
'EMPE0': [176, 176],
'JAUF0': [176, 176],
'DASSA0': [52, 52],
'JORL0': [171, 171],
'STDE0': [9, 9],
'FOJT0': [0, 23],
'CRZA0': [48, 49],
'FORG0': [158, 158],
'PTRN0': [155, 155],
'INDU0': [155, 155],
'TURB0': [155, 155],
'ERAB0': [155, 155],
'CCIA0': [107, 109],
'WALO0': [108, 108],
'AYGA0': [102, 102],
'BRRT0': [106, 106],
'KOBE0': [111, 111],
'DAUP0': [111, 111],
'LACR0': [111, 111],
'PISS0': [102, 102],
'DURR0': [102, 102],
'PALI0': [102, 102],
'NOVE0': [102, 102],
'CANT0': [102, 102],
'TREY0': [102, 102],
'PLAT0': [114, 114],
'CSTB0': [111, 111],
'LECT0': [111, 111],
'MING0': [111, 111],
'TCAS0': [61, 59],
'FOJE0': [24, 0],
'AYGS0': [74, 74],
'GUYE0': [74, 74],
'BIGO0': [74, 74],
'PTAR0': [74, 74],
'GARG0': [70, 70],
'MONM0': [70, 70],
'ESBO0': [75, 75],
'VIGO0': [75, 75],
'PINE0': [75, 75],
'MAUR0': [75, 75],
'VIGN0': [75, 75],
'NOST1': [70, 70],
'HLMH0': [70, 70],
'MORA0': [70, 70],
'MATI0': [70, 70],
'BLAC0': [35, 35],
'POLI1': [37, 37],
'MROL1': [35, 35],
'BCRG0': [79, 79],
'PAVE0': [79, 79],
'MARH0': [79, 79],
'ETAP0': [79, 79],
'CHAI0': [74, 74],
'BIDE0': [74, 74],
'ERRE0': [74, 74],
'BIMA0': [100, 100],
'CRRI0': [100, 100],
'BIDP0': [100, 100],
'BIDH0': [37, 37],
'MBID0': [26, 26],
'ERRE1': [26, 26],
'ECOL0': [52, 52],
'CHIR0': [52, 52],
'URON0': [52, 52],
'ITZU0': [57, 57],
'GUEH0': [31, 31],
'BERR0': [62, 62],
'HIRA0': [57, 57],
'JEGU0': [57, 57],
'RUIS0': [57, 57],
'JBAS1': [57, 57],
'ZABA0': [16, 16],
'JBAS0': [57, 57],
'PEMA0': [57, 57],
'ABBA1': [55, 55],
'CGPM0': [52, 52],
'ETCP0': [52, 52],
'URUB0': [52, 52],
'CARI0': [52, 52],
'PYRA0': [52, 52],
'BAST0': [52, 52],
'PITO0': [52, 52],
'TECH0': [140, 140],
'EDF0': [152, 152],
'PANO0': [152, 152],
'OYHA0': [26, 26],
'ATHE0': [26, 26],
'EMBR0': [26, 26],
'PLCT0': [26, 26],
'LNTQ0': [92, 92],
'NPLN0': [92, 92],
'RESP0': [92, 92],
'GARI0': [92, 92],
'SLIN0': [92, 92],
'MPOR0': [92, 92],
'HRGN0': [92, 92],
'BRDA0': [92, 92],
'ESCT0': [92, 92],
'AGUE0': [92, 92],
'MBOU0': [92, 92],
'LRHU0': [92, 92],
'MVIL0': [92, 92],
'BEDE0': [92, 92],
'MBUR0': [27, 24],
'VEGL0': [51, 51],
'VECL0': [51, 51],
'VBOU0': [51, 51],
'ARKI0': [51, 51],
'QBAS0': [51, 51],
'HILL0': [54, 52],
'EMAT0': [51, 51],
'DOCK0': [76, 76],
'HDEA0': [76, 76],
'LTXP0': [76, 76],
'KURU0': [76, 76],
'ETXE0': [152, 152],
'LTAS0': [152, 152],
'ABOU0': [78, 77],
'MBIS0': [79, 79],
'GNRL0': [76, 76],
'LRDT0': [79, 79],
'AXER0': [79, 79],
'XOKO0': [79, 79],
'AXRI0': [76, 76],
'HRR0': [39, 39],
'ERKT0': [79, 79],
'PERR0': [76, 76],
'BBOU0': [76, 76],
'MEND0': [76, 76],
'HRRI0': [40, 40],
'GONI0': [58, 48],
'LCRT0': [106, 106],
'MSTM0': [106, 106],
'PJRA0': [106, 106],
'QNEU0': [106, 106],
'BAGA0': [106, 106],
'LESC0': [106, 106],
'ZAAM0': [106, 106],
'BIAR0': [106, 106],
'PESP0': [25, 25],
'DSMA0': [58, 50],
'STRO0': [108, 108],
'GRON0': [108, 108],
'STRD0': [108, 108],
'DUMO0': [108, 108],
'BERTR0': [108, 108],
'OFTS0': [108, 108],
'MOND0': [108, 108],
'GARR1': [50, 50],
'PTJE0': [111, 111],
'TPLA0': [117, 114],
'LAHO0': [120, 120],
'HUGE0': [120, 120],
'MEMO0': [111, 111],
'CHPB0': [113, 113],
'SOEU0': [113, 113],
'BARS0': [113, 113],
'GREN0': [116, 116],
'LESS0': [116, 116],
'BAND0': [61, 61],
'DOUA0': [6, 6],
'JAME0': [5, 5],
'MTVL0': [6, 6],
'PAVC0': [3, 3],
'AJON0': [3, 3],
'PROM0': [3, 3],
'EMM0': [2, 1],
'BIARR0': [3, 3],
'MARG0': [3, 3],
'MAOU0': [3, 3],
'BANI1': [12, 12],
'BAL0': [12, 12],
'WALO1': [3, 5],
'CHANT0': [5, 5],
'PRAI1': [5, 5],
'ARTG1': [5, 5],
'COSE0': [5, 5],
'LVIG0': [14, 14],
'LASS0': [14, 14],
'LBDE0': [14, 14],
'PETC0': [5, 5],
'CASTA0': [5, 5],
'GLIZ0': [5, 5],
'GRDJ0': [6, 3],
'MAYE0': [9, 9],
'OCEA1': [9, 9],
'BELA0': [6, 5],
'OURT0': [7, 7],
'HAXU0': [7, 7],
'MISP0': [7, 7],
'LOST0': [7, 7],
'MAST0': [7, 7],
'AMLU0': [7, 7],
'OIAR0': [7, 7],
'LATX0': [1, 1],
'LATX1': [3, 3],
'PAUL1': [1, 1],
'TIVO1': [1, 1],
'CANR0': [3, 3],
'BRTE0': [3, 3],
'MAKI0': [3, 3],
'RLMG0': [3, 3],
'BRAU0': [3, 3],
'LHON0': [140, 140],
'OSHE0': [140, 140],
'FOUR0': [140, 140],
'SERR0': [140, 140],
'PARK0': [140, 140],
'FRIA1': [140, 140],
'VRDU0': [156, 156],
'HDVB0': [116, 116],
'BEPK0': [116, 116],
'GBTZ0': [116, 116],
'HELI1': [186, 186],
'PMAZ0': [70, 70],
'CHLE0': [70, 70],
'PRAD0': [70, 70],
'HRIS0': [70, 70],
'CRXB0': [116, 116],
'HGOB0': [116, 116],
'PHAL0': [116, 116],
'JBAR0': [70, 70],
'GARD0': [70, 70],
'STCH0': [86, 86],
'JUIN0': [86, 86],
'LVGNT0': [46, 46],
'ALSC0': [46, 46],
'IMPE0': [46, 46],
'REGI0': [92, 92],
'PHAR0': [46, 46],
'SARA0': [46, 46],
'COLI0': [86, 86],
'PELL0': [172, 172],
'FRSF0': [92, 92],
'FLPK0': [92, 92],
'CHEL0': [46, 46],
'CLMA0': [46, 46],
'BELY0': [46, 46],
'GRAM0': [46, 46],
'PSTR2': [46, 46],
'MIGR0': [46, 46],
'HIPP0': [46, 46],
'PSTR1': [46, 46],
'VOLT0': [46, 46],
'CHEL1': [46, 46],
'CLAG0': [40, 40],
'MAYO0': [40, 40],
'ETDO0': [80, 80],
'FERD0': [40, 40],
'MIM0': [40, 40],
'VERL0': [40, 40],
'CHFO0': [114, 114],
'GLCS0': [228, 228],
'PDB0': [392, 392],
'CVIE0': [196, 196],
'MAIR0': [392, 392],
'PMAY0': [114, 114],
'MRGO0': [114, 114],
'MSES0': [228, 228],
'EGST0': [228, 228],
'GLAI0': [114, 114],
'LABT0': [196, 196],
'QARE0': [82, 82],
'DUBQ0': [82, 82],
'FCH0': [164, 164],
'QSTE0': [82, 82],
'STVC0': [82, 82],
'ARG0': [82, 82],
'JDAM0': [82, 82],
'MARQ0': [82, 82],
'FLOR0': [66, 66],
'FOLL0': [132, 132]}
[9]:
plot_distribution_degree_analysis(TN)
Centrality analysis#
[10]:
plot_centrality_analysis(TN)
[11]:
map_centrality_analysis(TN)
Robustness analysis#
[12]:
plot_robustness_analysis(TN, precision=0.01)
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100%|██████████| 25/25 [00:00<00:00, 34663.67it/s]
100%|██████████| 18/18 [00:00<00:00, 44436.42it/s]
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100%|██████████| 11/11 [00:00<00:00, 40974.55it/s]
100%|██████████| 4/4 [00:00<00:00, 20841.26it/s]
100%|██████████| 87/87 [00:14<00:00, 6.16it/s]
Processing attack:: 60%|██████ | 3/5 [00:56<00:37, 18.65s/it]
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47%|████▋ | 286/606 [00:00<00:00, 1425.85it/s]
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46%|████▌ | 270/592 [00:00<00:00, 1352.15it/s]
69%|██████▊ | 406/592 [00:00<00:00, 1346.32it/s]
100%|██████████| 592/592 [00:00<00:00, 1318.99it/s]
3%|▎ | 3/87 [00:02<01:13, 1.14it/s]
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62%|██████▏ | 360/585 [00:00<00:00, 1626.03it/s]
100%|██████████| 585/585 [00:00<00:00, 1574.04it/s]
5%|▍ | 4/87 [00:03<01:10, 1.19it/s]
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25%|██▌ | 147/578 [00:00<00:00, 1466.07it/s]
51%|█████ | 294/578 [00:00<00:00, 1307.13it/s]
100%|██████████| 578/578 [00:00<00:00, 1445.16it/s]
6%|▌ | 5/87 [00:04<01:08, 1.20it/s]
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7%|▋ | 6/87 [00:05<01:06, 1.23it/s]
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57%|█████▋ | 322/564 [00:00<00:00, 1541.62it/s]
100%|██████████| 564/564 [00:00<00:00, 1530.66it/s]
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56%|█████▌ | 310/557 [00:00<00:00, 1427.18it/s]
100%|██████████| 557/557 [00:00<00:00, 1460.87it/s]
9%|▉ | 8/87 [00:06<01:02, 1.27it/s]
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34%|███▍ | 186/550 [00:00<00:00, 1840.85it/s]
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10%|█ | 9/87 [00:07<00:57, 1.35it/s]
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21%|██ | 18/87 [00:10<00:19, 3.63it/s]
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79%|███████▉ | 69/87 [00:13<00:00, 39.77it/s]
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100%|██████████| 95/95 [00:00<00:00, 26973.93it/s]
100%|██████████| 88/88 [00:00<00:00, 33946.36it/s]
100%|██████████| 81/81 [00:00<00:00, 32984.33it/s]
100%|██████████| 74/74 [00:00<00:00, 30438.22it/s]
89%|████████▊ | 77/87 [00:13<00:00, 50.16it/s]
100%|██████████| 67/67 [00:00<00:00, 19854.34it/s]
100%|██████████| 60/60 [00:00<00:00, 36241.11it/s]
100%|██████████| 53/53 [00:00<00:00, 35918.26it/s]
100%|██████████| 46/46 [00:00<00:00, 36631.48it/s]
100%|██████████| 39/39 [00:00<00:00, 20335.39it/s]
100%|██████████| 32/32 [00:00<00:00, 35715.20it/s]
100%|██████████| 25/25 [00:00<00:00, 28814.95it/s]
100%|██████████| 18/18 [00:00<00:00, 39036.96it/s]
100%|██████████| 11/11 [00:00<00:00, 25631.86it/s]
100%|██████████| 4/4 [00:00<00:00, 18766.46it/s]
100%|██████████| 87/87 [00:14<00:00, 6.21it/s]
Processing attack:: 80%|████████ | 4/5 [01:13<00:17, 17.83s/it]
0%| | 0/613 [00:00<?, ?it/s]
18%|█▊ | 112/613 [00:00<00:00, 1116.55it/s]
37%|███▋ | 224/613 [00:00<00:00, 1036.33it/s]
54%|█████▎ | 329/613 [00:00<00:00, 936.80it/s]
69%|██████▉ | 424/613 [00:00<00:00, 840.19it/s]
83%|████████▎ | 510/613 [00:00<00:00, 774.08it/s]
100%|██████████| 613/613 [00:00<00:00, 797.09it/s]
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34%|███▎ | 204/606 [00:00<00:00, 985.95it/s]
50%|█████ | 303/606 [00:00<00:00, 983.93it/s]
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52%|█████▏ | 309/599 [00:00<00:00, 1026.79it/s]
69%|██████▉ | 412/599 [00:00<00:00, 970.57it/s]
100%|██████████| 599/599 [00:00<00:00, 934.39it/s]
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33%|███▎ | 198/592 [00:00<00:00, 1969.03it/s]
67%|██████▋ | 395/592 [00:00<00:00, 1954.86it/s]
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63%|██████▎ | 367/585 [00:00<00:00, 1870.72it/s]
100%|██████████| 585/585 [00:00<00:00, 1807.23it/s]
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[13]:
map_robustness_analysis(TN)
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Machine learning pipeline to cluster the network#
[14]:
gw = GraphWave()
emb_df = gw.get_embedding_df(TN.get_higher_complexity())
clusters_dct = get_clusters(TN.get_higher_complexity(), type='kmeans', embedding=emb_df, k=3)
plot_clusters_embedding(emb_df, clusters_dct)
map_clusters(TN, clusters_dct)
/home/anthony/Desktop/Group Project/XYZnetwork_lib/venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:
The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
GNN pipeline to cluster the network#
Create GNN model#
[15]:
args = {
"node_features" : ["position"], # choices are ["degree_one_hot", "one_hot", "constant", "pagerank", "degree", "betweenness", "closeness", "eigenvector", "clustering", "position", "distance"]
"edge_attrs" : ["departure_time"], # choices are ["distance", "dep_time", "arr_time"]
"train_ratio" : 0.8,
"val_ratio" : 0.2,
"layers" : 2,
"model": "gat", # choices are ["gcn", "gin", "gat", "sage"]
"lr" : 0.001,
"epochs" : 200,
"loss" : "infonce",
"augment_list" : ["edge_perturbation", "node_dropping"],
}
args = GNNConfig(args)
[16]:
# Create data
data = create_data_from_transport_network(TN.graph, TN, args).to(args.device)
# Create model
ssl_model = SSL_GNN(data.num_node_features, args).to(args.device)
# Create the optimizer
optimizer = torch.optim.Adam(ssl_model.parameters(), lr=args.lr)
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Train model#
[17]:
# Train model
train_self_supervised(data, ssl_model, optimizer, args)
Epoch: 1, Train Loss: 6.175541400909424, Val Loss: 4.6401472091674805
Epoch: 2, Train Loss: 6.165186405181885, Val Loss: 4.621582508087158
Epoch: 3, Train Loss: 6.137016773223877, Val Loss: 4.573115348815918
Epoch: 4, Train Loss: 6.137843608856201, Val Loss: 4.508846759796143
Epoch: 5, Train Loss: 6.140758514404297, Val Loss: 4.412999153137207
Epoch: 6, Train Loss: 6.066816806793213, Val Loss: 4.322457790374756
Epoch: 7, Train Loss: 6.084362983703613, Val Loss: 4.187949180603027
Epoch: 8, Train Loss: 6.020055294036865, Val Loss: 4.065433979034424
Epoch: 9, Train Loss: 5.951279640197754, Val Loss: 3.9332938194274902
Epoch: 10, Train Loss: 5.905524730682373, Val Loss: 3.837998628616333
Epoch: 11, Train Loss: 5.846800804138184, Val Loss: 3.7115979194641113
Epoch: 12, Train Loss: 5.765684604644775, Val Loss: 3.654060125350952
Epoch: 13, Train Loss: 5.711491584777832, Val Loss: 3.5846762657165527
Epoch: 14, Train Loss: 5.643587589263916, Val Loss: 3.599376678466797
Epoch: 15, Train Loss: 5.621657848358154, Val Loss: 3.6026875972747803
Epoch: 16, Train Loss: 5.516519546508789, Val Loss: 3.5432722568511963
Epoch: 17, Train Loss: 5.495982646942139, Val Loss: 3.5931057929992676
Epoch: 18, Train Loss: 5.44849157333374, Val Loss: 3.5444960594177246
Epoch: 19, Train Loss: 5.459173202514648, Val Loss: 3.5548784732818604
Epoch: 20, Train Loss: 5.3404316902160645, Val Loss: 3.547093629837036
Epoch: 21, Train Loss: 5.314237594604492, Val Loss: 3.547714948654175
Epoch: 22, Train Loss: 5.305752277374268, Val Loss: 3.542719602584839
Epoch: 23, Train Loss: 5.256820201873779, Val Loss: 3.505249261856079
Epoch: 24, Train Loss: 5.239745140075684, Val Loss: 3.5143473148345947
Epoch: 25, Train Loss: 5.205666542053223, Val Loss: 3.5303795337677
Epoch: 26, Train Loss: 5.1911139488220215, Val Loss: 3.5188703536987305
Epoch: 27, Train Loss: 5.177889347076416, Val Loss: 3.5028531551361084
Epoch: 28, Train Loss: 5.139930725097656, Val Loss: 3.5208795070648193
Epoch: 29, Train Loss: 5.14262580871582, Val Loss: 3.532407283782959
Epoch: 30, Train Loss: 5.10964298248291, Val Loss: 3.506572723388672
Epoch: 31, Train Loss: 5.091980934143066, Val Loss: 3.4889495372772217
Epoch: 32, Train Loss: 5.095240116119385, Val Loss: 3.493278741836548
Epoch: 33, Train Loss: 5.0643415451049805, Val Loss: 3.4744207859039307
Epoch: 34, Train Loss: 5.078542232513428, Val Loss: 3.472468137741089
Epoch: 35, Train Loss: 5.053869247436523, Val Loss: 3.4628825187683105
Epoch: 36, Train Loss: 5.033364772796631, Val Loss: 3.462650775909424
Epoch: 37, Train Loss: 5.01200008392334, Val Loss: 3.437474012374878
Epoch: 38, Train Loss: 5.00202751159668, Val Loss: 3.40383243560791
Epoch: 39, Train Loss: 4.984353542327881, Val Loss: 3.4068446159362793
Epoch: 40, Train Loss: 4.981302738189697, Val Loss: 3.3649938106536865
Epoch: 41, Train Loss: 4.958160877227783, Val Loss: 3.3868987560272217
Epoch: 42, Train Loss: 4.963011264801025, Val Loss: 3.4005274772644043
Epoch: 43, Train Loss: 4.951054573059082, Val Loss: 3.395815372467041
Epoch: 44, Train Loss: 4.917444705963135, Val Loss: 3.352619171142578
Epoch: 45, Train Loss: 4.910520553588867, Val Loss: 3.3633506298065186
Epoch: 46, Train Loss: 4.8997344970703125, Val Loss: 3.338639974594116
Epoch: 47, Train Loss: 4.9124932289123535, Val Loss: 3.323990821838379
Epoch: 48, Train Loss: 4.877128601074219, Val Loss: 3.3107619285583496
Epoch: 49, Train Loss: 4.869313716888428, Val Loss: 3.320084810256958
Epoch: 50, Train Loss: 4.8800835609436035, Val Loss: 3.313464403152466
Epoch: 51, Train Loss: 4.864095211029053, Val Loss: 3.2920382022857666
Epoch: 52, Train Loss: 4.865602970123291, Val Loss: 3.2860617637634277
Epoch: 53, Train Loss: 4.85732889175415, Val Loss: 3.3031105995178223
Epoch: 54, Train Loss: 4.858405590057373, Val Loss: 3.288318634033203
Epoch: 55, Train Loss: 4.838765621185303, Val Loss: 3.2894110679626465
Epoch: 56, Train Loss: 4.835594654083252, Val Loss: 3.2820382118225098
Epoch: 57, Train Loss: 4.828744411468506, Val Loss: 3.2698450088500977
Epoch: 58, Train Loss: 4.830524921417236, Val Loss: 3.2688705921173096
Epoch: 59, Train Loss: 4.823407173156738, Val Loss: 3.2678515911102295
Epoch: 60, Train Loss: 4.808182716369629, Val Loss: 3.2587931156158447
Epoch: 61, Train Loss: 4.818406105041504, Val Loss: 3.2607975006103516
Epoch: 62, Train Loss: 4.790672779083252, Val Loss: 3.2627532482147217
Epoch: 63, Train Loss: 4.796024799346924, Val Loss: 3.2653095722198486
Epoch: 64, Train Loss: 4.778975009918213, Val Loss: 3.269767999649048
Epoch: 65, Train Loss: 4.793673038482666, Val Loss: 3.2390100955963135
Epoch: 66, Train Loss: 4.784870624542236, Val Loss: 3.26735782623291
Epoch: 67, Train Loss: 4.775079727172852, Val Loss: 3.2326619625091553
Epoch: 68, Train Loss: 4.777248382568359, Val Loss: 3.2375247478485107
Epoch: 69, Train Loss: 4.7871623039245605, Val Loss: 3.23917818069458
Epoch: 70, Train Loss: 4.778112888336182, Val Loss: 3.242129325866699
Epoch: 71, Train Loss: 4.76801872253418, Val Loss: 3.207897186279297
Epoch: 72, Train Loss: 4.765249729156494, Val Loss: 3.2239482402801514
Epoch: 73, Train Loss: 4.75560188293457, Val Loss: 3.224822998046875
Epoch: 74, Train Loss: 4.762436389923096, Val Loss: 3.2127676010131836
Epoch: 75, Train Loss: 4.75024938583374, Val Loss: 3.209044933319092
Epoch: 76, Train Loss: 4.741896152496338, Val Loss: 3.212468385696411
Epoch: 77, Train Loss: 4.742732524871826, Val Loss: 3.2058560848236084
Epoch: 78, Train Loss: 4.7404327392578125, Val Loss: 3.207453727722168
Epoch: 79, Train Loss: 4.741640567779541, Val Loss: 3.1954760551452637
Epoch: 80, Train Loss: 4.727570533752441, Val Loss: 3.1868584156036377
Epoch: 81, Train Loss: 4.735937595367432, Val Loss: 3.2000036239624023
Epoch: 82, Train Loss: 4.7315263748168945, Val Loss: 3.1774449348449707
Epoch: 83, Train Loss: 4.718042373657227, Val Loss: 3.193479537963867
Epoch: 84, Train Loss: 4.714792251586914, Val Loss: 3.1850171089172363
Epoch: 85, Train Loss: 4.7087297439575195, Val Loss: 3.179201602935791
Epoch: 86, Train Loss: 4.703948497772217, Val Loss: 3.160034418106079
Epoch: 87, Train Loss: 4.689323902130127, Val Loss: 3.1622252464294434
Epoch: 88, Train Loss: 4.691718101501465, Val Loss: 3.1714465618133545
Epoch: 89, Train Loss: 4.678915023803711, Val Loss: 3.155579090118408
Epoch: 90, Train Loss: 4.681928634643555, Val Loss: 3.143484115600586
Epoch: 91, Train Loss: 4.682182788848877, Val Loss: 3.1388909816741943
Epoch: 92, Train Loss: 4.678615570068359, Val Loss: 3.13250732421875
Epoch: 93, Train Loss: 4.666132926940918, Val Loss: 3.1212263107299805
Epoch: 94, Train Loss: 4.674240589141846, Val Loss: 3.126741409301758
Epoch: 95, Train Loss: 4.6670379638671875, Val Loss: 3.125051975250244
Epoch: 96, Train Loss: 4.6579437255859375, Val Loss: 3.136636734008789
Epoch: 97, Train Loss: 4.659397125244141, Val Loss: 3.1238229274749756
Epoch: 98, Train Loss: 4.660508632659912, Val Loss: 3.134683609008789
Epoch: 99, Train Loss: 4.650249004364014, Val Loss: 3.1199448108673096
Epoch: 100, Train Loss: 4.632353782653809, Val Loss: 3.111192464828491
Epoch: 101, Train Loss: 4.65403413772583, Val Loss: 3.1257665157318115
Epoch: 102, Train Loss: 4.646122455596924, Val Loss: 3.1219727993011475
Epoch: 103, Train Loss: 4.626082897186279, Val Loss: 3.107996702194214
Epoch: 104, Train Loss: 4.62863826751709, Val Loss: 3.108457326889038
Epoch: 105, Train Loss: 4.628318786621094, Val Loss: 3.108051300048828
Epoch: 106, Train Loss: 4.648366928100586, Val Loss: 3.1087100505828857
Epoch: 107, Train Loss: 4.636220932006836, Val Loss: 3.1174275875091553
Epoch: 108, Train Loss: 4.616308689117432, Val Loss: 3.1174399852752686
Epoch: 109, Train Loss: 4.617178916931152, Val Loss: 3.1161653995513916
Epoch: 110, Train Loss: 4.627208709716797, Val Loss: 3.101362943649292
Epoch: 111, Train Loss: 4.616775989532471, Val Loss: 3.1151163578033447
Epoch: 112, Train Loss: 4.61387825012207, Val Loss: 3.096498966217041
Epoch: 113, Train Loss: 4.612590789794922, Val Loss: 3.0964856147766113
Epoch: 114, Train Loss: 4.599580764770508, Val Loss: 3.101086378097534
Epoch: 115, Train Loss: 4.593467712402344, Val Loss: 3.0920379161834717
Epoch: 116, Train Loss: 4.605584144592285, Val Loss: 3.0890588760375977
Epoch: 117, Train Loss: 4.592251300811768, Val Loss: 3.0934338569641113
Epoch: 118, Train Loss: 4.599854469299316, Val Loss: 3.079991102218628
Epoch: 119, Train Loss: 4.5932817459106445, Val Loss: 3.0885348320007324
Epoch: 120, Train Loss: 4.597029685974121, Val Loss: 3.0941150188446045
Epoch: 121, Train Loss: 4.595119476318359, Val Loss: 3.088383436203003
Epoch: 122, Train Loss: 4.587526798248291, Val Loss: 3.096750497817993
Epoch: 123, Train Loss: 4.579897880554199, Val Loss: 3.0796730518341064
Epoch: 124, Train Loss: 4.586674690246582, Val Loss: 3.093437671661377
Epoch: 125, Train Loss: 4.58022928237915, Val Loss: 3.0934972763061523
Epoch: 126, Train Loss: 4.575448513031006, Val Loss: 3.092965602874756
Epoch: 127, Train Loss: 4.582917213439941, Val Loss: 3.081746816635132
Epoch: 128, Train Loss: 4.570718288421631, Val Loss: 3.0880918502807617
Epoch: 129, Train Loss: 4.581130027770996, Val Loss: 3.086928606033325
Epoch: 130, Train Loss: 4.568184852600098, Val Loss: 3.087228775024414
Epoch: 131, Train Loss: 4.584911346435547, Val Loss: 3.0791213512420654
Epoch: 132, Train Loss: 4.573314666748047, Val Loss: 3.085252285003662
Epoch: 133, Train Loss: 4.567775249481201, Val Loss: 3.084843397140503
Epoch: 134, Train Loss: 4.579724311828613, Val Loss: 3.082167863845825
Epoch: 135, Train Loss: 4.569276332855225, Val Loss: 3.0829482078552246
Epoch: 136, Train Loss: 4.565314769744873, Val Loss: 3.0809290409088135
Epoch: 137, Train Loss: 4.574264049530029, Val Loss: 3.0784876346588135
Epoch: 138, Train Loss: 4.565886497497559, Val Loss: 3.082606315612793
Epoch: 139, Train Loss: 4.5717339515686035, Val Loss: 3.074709892272949
Epoch: 140, Train Loss: 4.563697338104248, Val Loss: 3.0775835514068604
Epoch: 141, Train Loss: 4.564490795135498, Val Loss: 3.068920135498047
Epoch: 142, Train Loss: 4.571242332458496, Val Loss: 3.058178663253784
Epoch: 143, Train Loss: 4.553027629852295, Val Loss: 3.0721323490142822
Epoch: 144, Train Loss: 4.561403274536133, Val Loss: 3.0747182369232178
Epoch: 145, Train Loss: 4.553050994873047, Val Loss: 3.0812172889709473
Epoch: 146, Train Loss: 4.559077739715576, Val Loss: 3.081716775894165
Epoch: 147, Train Loss: 4.558478355407715, Val Loss: 3.0709331035614014
Epoch: 148, Train Loss: 4.5468430519104, Val Loss: 3.0778684616088867
Epoch: 149, Train Loss: 4.5536980628967285, Val Loss: 3.081664562225342
Epoch: 150, Train Loss: 4.553882598876953, Val Loss: 3.079587936401367
Epoch: 151, Train Loss: 4.558004379272461, Val Loss: 3.058750867843628
Epoch: 152, Train Loss: 4.552406311035156, Val Loss: 3.0835866928100586
Epoch: 153, Train Loss: 4.553356647491455, Val Loss: 3.063716173171997
Epoch: 154, Train Loss: 4.548372268676758, Val Loss: 3.0638179779052734
Epoch: 155, Train Loss: 4.548031330108643, Val Loss: 3.074063301086426
Epoch: 156, Train Loss: 4.539443016052246, Val Loss: 3.068603754043579
Epoch: 157, Train Loss: 4.543023586273193, Val Loss: 3.0723633766174316
Epoch: 158, Train Loss: 4.541362762451172, Val Loss: 3.064699172973633
Epoch: 159, Train Loss: 4.539776802062988, Val Loss: 3.067152976989746
Epoch: 160, Train Loss: 4.543396472930908, Val Loss: 3.067138195037842
Epoch: 161, Train Loss: 4.542850017547607, Val Loss: 3.074418544769287
Epoch: 162, Train Loss: 4.532594680786133, Val Loss: 3.077169895172119
Epoch: 163, Train Loss: 4.542054176330566, Val Loss: 3.056513786315918
Epoch: 164, Train Loss: 4.529015064239502, Val Loss: 3.057905435562134
Epoch: 165, Train Loss: 4.532743453979492, Val Loss: 3.0556800365448
Epoch: 166, Train Loss: 4.526042938232422, Val Loss: 3.0694518089294434
Epoch: 167, Train Loss: 4.533532619476318, Val Loss: 3.0621867179870605
Epoch: 168, Train Loss: 4.5321149826049805, Val Loss: 3.063549041748047
Epoch: 169, Train Loss: 4.52910041809082, Val Loss: 3.051084280014038
Epoch: 170, Train Loss: 4.538821220397949, Val Loss: 3.0678415298461914
Epoch: 171, Train Loss: 4.527117729187012, Val Loss: 3.058896541595459
Epoch: 172, Train Loss: 4.525818824768066, Val Loss: 3.053851842880249
Epoch: 173, Train Loss: 4.528687000274658, Val Loss: 3.0632615089416504
Epoch: 174, Train Loss: 4.525545120239258, Val Loss: 3.049077033996582
Epoch: 175, Train Loss: 4.527365207672119, Val Loss: 3.0633230209350586
Epoch: 176, Train Loss: 4.521368980407715, Val Loss: 3.0527305603027344
Epoch: 177, Train Loss: 4.528260707855225, Val Loss: 3.0569040775299072
Epoch: 178, Train Loss: 4.531463623046875, Val Loss: 3.0460755825042725
Epoch: 179, Train Loss: 4.5167317390441895, Val Loss: 3.0561931133270264
Epoch: 180, Train Loss: 4.523371696472168, Val Loss: 3.059535026550293
Epoch: 181, Train Loss: 4.515563011169434, Val Loss: 3.0557353496551514
Epoch: 182, Train Loss: 4.52605676651001, Val Loss: 3.0651724338531494
Epoch: 183, Train Loss: 4.518230438232422, Val Loss: 3.064960479736328
Epoch: 184, Train Loss: 4.521439075469971, Val Loss: 3.047875165939331
Epoch: 185, Train Loss: 4.520839214324951, Val Loss: 3.057209014892578
Epoch: 186, Train Loss: 4.5212626457214355, Val Loss: 3.058290719985962
Epoch: 187, Train Loss: 4.515894889831543, Val Loss: 3.049534320831299
Epoch: 188, Train Loss: 4.523036479949951, Val Loss: 3.052393913269043
Epoch: 189, Train Loss: 4.5323920249938965, Val Loss: 3.043151378631592
Epoch: 190, Train Loss: 4.522473335266113, Val Loss: 3.039036989212036
Epoch: 191, Train Loss: 4.513176441192627, Val Loss: 3.059544563293457
Epoch: 192, Train Loss: 4.516465663909912, Val Loss: 3.0428316593170166
Epoch: 193, Train Loss: 4.516859531402588, Val Loss: 3.039519786834717
Epoch: 194, Train Loss: 4.51363468170166, Val Loss: 3.047665596008301
Epoch: 195, Train Loss: 4.512304782867432, Val Loss: 3.0405797958374023
Epoch: 196, Train Loss: 4.522615432739258, Val Loss: 3.0372507572174072
Epoch: 197, Train Loss: 4.516915321350098, Val Loss: 3.0554823875427246
Epoch: 198, Train Loss: 4.515917778015137, Val Loss: 3.056680202484131
Epoch: 199, Train Loss: 4.509296417236328, Val Loss: 3.037299633026123
Epoch: 200, Train Loss: 4.521661281585693, Val Loss: 3.0372421741485596
Best Val Loss: 3.0372421741485596
Get embedding#
[18]:
emb = get_graph_embedding(data, ssl_model)
clusters_dct = get_clusters(TN.get_higher_complexity(), type='kmeans', embedding=emb, k=24)
plot_clusters_embedding(emb, clusters_dct)
map_clusters(TN, clusters_dct)
/home/anthony/Desktop/Group Project/XYZnetwork_lib/venv/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:
The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
[18]: